Requirements Prioritization Techniques Review and Analysis

Qaddoura, Raneem; Abu-Srhan, Alaa; Qasem, Mais Haj; Hudaib, Amjad

doi:10.1109/ictcs.2017.55

Cited by 7 publications

(5 citation statements)

References 23 publications

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“… Criticality‐driven issue prioritization in RE sessions . Requirements prioritization is an important activity of the requirement engineering (RE) process 59 . In RE sessions, developers are tasked to determine and prioritize the requirements of the software system, trying to integrate end‐users needs 58 .…”

Section: Discussionmentioning

confidence: 99%

“…Requirements prioritization is an important activity of the requirement engineering (RE) process. 59 In RE sessions, developers are tasked to determine and prioritize the requirements of the software system, trying to integrate end-users needs. 58 Our results can be used to support collaborative decision-making processes in requirements prioritization.…”

Section: Discussionmentioning

confidence: 99%

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Investigating the criticality of user‐reported issues through their relations with app rating

Sorbo

Grano

Visaggio

et al. 2020

J Software Evolu Process

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App quality impacts user experience and satisfaction. As a consequence, both app ratings and user feedback reported in app reviews are directly influenced by the user-perceived app quality. Through an empirical study involving 210,517 reviews related to 317 Android apps, in this paper, we experiment with the combined usage of app rating and user reviews analysis (i) to investigate the most important factors influencing the perceived app quality, (ii) focusing on the topics discussed in user review that most relate with app rating. Besides, we investigate whether specific code quality metrics could be monitored to prevent the rising of negative user feedback (i.e., types of user review comments), connected with low ratings. Our study demonstrates that user comments reporting bugs are negatively correlated with the rating, while reviews reportingfeature requests do not. Interestingly, depending on the app category, we observed that different kinds of issues have rather different relationships with the rating and the user-perceived quality of the app. In particular, we observe that for specific app categories (e.g., communication), some code quality factors have significant relationships with the raising of certain types of feedback, which, in turn, are negatively connected with app ratings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Investigating the criticality of user‐reported issues through their relations with app rating

Sorbo

Grano

Visaggio

et al. 2020

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…In these techniques, human input is very important. Qaddoura et al [40] reviewed prioritization techniques and also shed light on the contribution of ML to the topic. ML can be used for automated analysis of these large set of software requirements prioritization, as well as in helping to improve existing techniques.…”

Section: B Requirements Specification and Analysismentioning

confidence: 99%

A Bird's Eye View on Requirements Engineering and Machine Learning

Iqbal

Elahidoost

Levi

2018

2018 25th Asia-Pacific Software Engineering Conference (APSEC)

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Machine learning (ML) has demonstrated practical impact in a variety of application domains. Software engineering is a fertile domain where ML is helping in automating different tasks. In this paper, our focus is the intersection of software requirement engineering (RE) and ML. To obtain an overview of how ML is helping RE and the research trends in this area, we have surveyed a large number of research articles. We found that the impact of ML can be observed in requirement elicitation, analysis and specification, validation and management. Furthermore, in these categories, we discuss the specific problem solved by ML, the features and ML algorithms used as well as datasets, when available. We outline lessons learned and envision possible future directions for the domain.

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“…Many conventional software requirement prioritization techniques have been proposed by authors [12], [14], [29], [32], [37], [43], [47], [51], [55], [57]. These methods are quantitative as well as subjective in nature.…”

Section: Introductionmentioning

confidence: 99%

“…Existing requirements prioritization techniques suffer from several problems such as complexity of application, expensive in computations, lack of automation, scalability, ambiguity, and uncertainty [12], [37], [47], [53], [59]. Software initiatives might come with several risks, assumptions, and requirements that are incompatible.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

Anwar,

Bashir

2023

IEEE Access

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Software requirements show what the customer desires his software to do. They are the first stepping stone towards a successful software development project. With the increasing complexity of the software due to its size and feature base, it is vital to prioritize the requirements for efficient utilization of development resources. To achieve this, industrial organizations are devising new strategies and improved solutions even with the help of artificial intelligence (AI) tool set. Existing requirements prioritization techniques are human-intensive and suffer from several limitations like overlapping outcomes, scalability problems, time consumption, inaccuracy, and so on. Some of the problems can be solved with the inclusion of artificial intelligence algorithms and strategies. For this several AI-based requirements prioritization techniques have been proposed by applying Genetic Algorithms, Fuzzy Logic, Ant Colony Optimization, and Machine Learning. Literature witnesses some good review studies and surveys on conventional prioritization techniques but there exists none for AI-based techniques that identify not only their strengths but also their weaknesses, advantages of machine learning techniques over other AI-based requirements prioritization techniques, and limitations of applying AI-based techniques in requirements prioritization. This study presents a systematic literature review (SLR) of AI-based requirements prioritization approaches covering 46 papers published from 2000 to 2021. We have given this literature review a new dimension by conducting a parametric analysis of AI-based requirements prioritization techniques and we have identified these parameters after a thorough literature study. Some of the chosen parameters are generic (related to the prioritization process) and some are specific (related to AI techniques). This study has greatly helped us draw a clear line among AI-based techniques to show their domain of application to gain maximum advantage. Our findings will assist researchers, requirement analysts, and other stakeholders in making a wise decision to select the best requirements prioritization technique to gain optimal results.

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Requirements Prioritization Techniques Review and Analysis

Cited by 7 publications

References 23 publications

Investigating the criticality of user‐reported issues through their relations with app rating

Investigating the criticality of user‐reported issues through their relations with app rating

A Bird's Eye View on Requirements Engineering and Machine Learning

A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

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